From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes

Julien Apala N’drin¹, Ouagnina Hili²

¹Laboratory of Applied Mathematics and Computer Science, University Felix Houphouët Boigny, Abidjan, Côte d’Ivoire.
²Laboratory of Mathematics and New Technologies of Information, National Polytechnique Institute Houphouët-Boigny of Yamoussoukro, Yamoussoukro, Côte d’Ivoire.

DOI: 10.4236/am.2015.69142                

Abstract

The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process. Under this condition, we define an estimator of the density based on kernel function and study his properties (almost sure convergence and asymptotic normality). After, using the estimator of the density, we construct the minimum Hellinger distance estimator of the parameters of the diffusion process and establish the almost sure convergence and the asymptotic normality of this estimator. To illustrate the properties of the estimator of the parameters, we apply the method to two examples of multidimensional diffusion processes.

Keywords

Hellinger Distance Estimation, Multidimensional Diffusion Processes, Strong Mixing Process, Consistence, Asymptotic Normality

Conflicts of Interest

The authors declare no conflicts of interest.

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